Bayesian Deep Learning and Uncertainty in Object Detection

In order to fully integrate deep
learning into robotics, it is important that deep learning systems
can reliably estimate the uncertainty in their predictions.
This would allow robots to treat a deep neural network
like any other sensor, and use the established Bayesian
techniques to fuse the network’s predictions
with prior knowledge or other sensor measurements, or to
accumulate information over time.

Deep learning systems, e.g. for classification or detection, typically return scores
from their softmax layers that are proportional to the system’s
confidence, but are not calibrated probabilities, and therefore
not useable in a Bayesian sensor fusion framework.

Current approaches towards uncertainty estimation for
deep learning are calibration techniques, or
Bayesian deep learning with approximations such
as Monte Carlo Dropout or ensemble methods.

Our work focusses on Bayesian Deep Learning approaches for the specific use case of object detection on a robot in open-set conditions.

Publications

Dropout Sampling for Robust Object Detection in Open-Set Conditions
Dimity Miller,
Lachlan Nicholson,
Feras Dayoub,
Niko Sünderhauf.
In Proc. of IEEE International Conference on Robotics and Automation (ICRA),
2018.
Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an
approximation technique for Bayesian Deep Learning and evaluated for image classification
and regression tasks. This paper investigates the utility of Dropout Sampling for object
detection for the first time. We demonstrate how label uncertainty can be extracted from a
state-of-the-art object detection system via Dropout Sampling. We show that this uncertainty
can be utilized to increase object detection performance under the open-set conditions that
are typically encountered in robotic vision. We evaluate this approach on a large synthetic
dataset with 30,000 images, and a real-world dataset captured by a mobile robot in a
versatile campus environment.

Dropout Variational Inference Improves Object Detection in Open-Set Conditions
Dimity Miller,
Lachlan Nicholson,
Feras Dayoub,
Niko Sünderhauf.
In Proc. of NIPS Workshop on Bayesian Deep Learning,
2017.
One of the biggest current challenges of visual object detection is reliable operation in open-set
conditions. One way to handle the open-set problem is to utilize the uncertainty of the model to reject predictions
with low probability. Bayesian Neural Networks (BNNs), with variational inference commonly
used as an approximation, is an established approach to estimate model uncertainty. Here we extend the concept of Dropout sampling to object detection for the first time. We evaluate
Bayesian object detection on a large synthetic and a real-world dataset and show how the estimated
label uncertainty can be utilized to increase object detection performance under open-set conditions.

Episode-Based Active Learning with Bayesian Neural Networks
Feras Dayoub,
Niko Sünderhauf,
Peter Corke.
In Workshop on Deep Learning for Robotic Vision, Conference on Computer Vision and Pattern Recognition (CVPR),
2017.
We investigate different strategies for active learning
with Bayesian deep neural networks. We focus our analysis
on scenarios where new, unlabeled data is obtained episodically,
such as commonly encountered in mobile robotics
applications. An evaluation of different strategies for acquisition,
updating, and final training on the CIFAR-10 dataset
shows that incremental network updates with final training
on the accumulated acquisition set are essential for best
performance, while limiting the amount of required human
labeling labor.